关键词: 脑电信号, 快速序列视觉呈现, 稳态视觉诱发, 决策树, 支持向量机, 多类检测

Abstract:

A new method based on the combination of?Rapid Serial Visual Presentation（RSVP） and Steady-State Visually Evoked Potential（SSVEP） for Electroencephalogram（EEG） stimulation and multi-class events detection is presented in this paper. The artifacts and noise of the raw EEG signals are removed by preprocessing including potential re-reference, baseline removal and spatial filtering. Machine learning algorithms such as Bagging Tree（BT） and Supported Vector Machine（SVM） are used to detect multi-class events of EEG signals induced by double stimuli on 14 subjects. The experimental results show that the EEG signals induced by the combined stimulation have good multi-class separability, which provides a new and effective way to develop the hybrid brain-computer interface application based on RSVP and SSVEP combination paradigm. The outcome also proves that the performance of the BT and SVM models for multi-class recognition of EEG signals induced by RSVP and SSVEP is obviously better than that of the Traditional Canonical Correlation analysis（CCA） algorithm.

Key words: Electroencephalogram（EEG）, Rapid Serial Visual Presentation（RSVP）, Steady-State Visually Evoked Potential（SSVEP）, Bagging Tree（BT）, Supported Vector Machine（SVM）, multi-class detection